The broad goals of this project are to understand the set of computations performed by a neural circuit. The mammalian retina is chosen as a model system, because of our ability to achieve large-scale recordings of its output while stimulating with realistic patterns of light, as well as its intricate and well-characterized anatomy. The retina's representation of visual stimuli changes dramatically between the photoreceptors, which use a camera-like representation where each cell is a pixel, to the ganglion cells, which represent the image with a complex and highly overlapping basis set of visual features. We seek to understand how visual information is organized within and among the retina's parallel visual channels. The Specific Aims are: 1) characterize the diversity and stereotypy of the visual features represented within the population of ganglion cells by performing functional classification; 2) use recently developed techniques of information-theoretic analysis to measure the correlation and redundancy in large populations of ganglion cells; 3) explore how correlated populations formed both within and among parallel channels can be used to perform fundamental visual tasks, such as spatial localization, shape discrimination, and temporal pattern recognition. A detailed knowledge of how the population of retinal ganglion cells represents the visual world is of fundamental interest to neuroscience and is also important for guiding the development of a retinal prosthesis to restore vision successfully.